Online Identification and Alignment of MIMO Cross Directional Controlled Processes Using Second Order Statistics
Bibliographic record
Abstract
Among the factors influencing the performance of multi-input multi-output (MIMO) systems such as paper machines, accurate spatial alignment of the actuators with their corresponding measurement points is a key issue in cross directional (CD) control. This mapping is a stochastic, non-linear and time-varying problem. Most current methods of alignment require a manual open-loop bump test. Several actuators are excited to perform the bump test, then the observed peaks are assigned to the excited actuators. This paper uses the second-order statistical technique of the blind source separation methods to make the transition from a manual, open-loop bump test to closed-loop adaptive online mapping. This procedure estimates the mixing matrix by spatial decorrelation of the noise and source signal. The mixing matrix is a function of input autocorrelation input and output cross-correlation. The main advantage of this method comes from the fact that we have direct access to the source signals and outputs. The method's robustness in cases of spatially colored noise makes it applicable to the CD control of paper machines.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".